LAD variable selection for linear models with randomly censored data |
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| Authors: | Zhangong Zhou Rong Jiang Weimin Qian |
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| Affiliation: | 1. Department of Statistics, Jiaxing University, Zhejiang, 314001, People’s Republic of China
2. Department of Mathematics, Tongji University, Shanghai, 200092, People’s Republic of China
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| Abstract: | The least absolute deviations (LAD) variable selection for linear models with randomly censored data is studied through the Lasso. The proposed procedure can select significant variables in the parameters. With appropriate selection of the tuning parameters, we establish the consistency of this procedure and the oracle property of the resulting estimators. Simulation studies are conducted to compare the proposed procedure with an inverse-censoring-probability weighted LAD LASSO-estimator. |
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